How Do You Feel? Managing Emotional Reaction, Conveyance, and Detachment on Facebook and Instagram
Bibliographic record
Abstract
Studies of social media and its uses have focused on how it shapes behavior but less so with emotion. Overcoming this limitation, this article investigates the role of emotion in understanding and shaping actions online, and how, conversely, different uses of social media are leveraged to manage and express emotions, focusing on Facebook and Instagram. To this end, this article draws on 24 in-depth interviews with youth users in Hong Kong to excavate practices of emotional labor and management online, which reveal (1) strategies to manage emotional reactions, centering on critical distance; (2) strategies to manage emotional conveyance by manipulating the temporality of the content they produce; and (3) the creation of a digital blasé that consisted of the atmosphere of Facebook and Instagram, sustained by general emotional detachment, the perceived need to detach, and a sense of “watchedness”. Throughout, emotional detachment was the default state that users entered into when using Facebook and Instagram, as an anticipatory reaction to the emotional exhaustion imposed by imagined content and into which they inevitably returned.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".